nanoJOBS – Careers in Nanotechnology

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Imec is the world-leading research and innovation hub in nanoelectronics and digital technologies. The combination of our widely acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique. By leveraging our world-class infrastructure and local and global ecosystem of partners across a multitude of industries, we create groundbreaking innovation in application domains such as healthcare, smart cities and mobility, logistics and manufacturing, and energy.

As a trusted partner for companies, start-ups and universities we bring together close to 3,500 brilliant minds from over 70 nationalities. Imec is headquartered in Leuven, Belgium and also has distributed R&D groups at a number of Flemish universities, in the Netherlands, Taiwan, USA, China, and offices in India and Japan. All of these particular traits make imec to be a top-class employer. To strengthen this position as a leading player in our field, we are looking for those passionate talents that make the difference!

Job Description:

The characteristics of certain deep learning workloads (e.g. multi-layer perceptrons, convolutional neural networks, hierarchical temporal memories), are being gradually picked up by the hardware design community in an attempt to create efficient integrated solutions using non-volatile memory elements. This position will involve algorithm refinements on neural networks, so that these can be mapped optimally on non-volatile memory fabrics. This will involve verification of system-level choices related to neural network topology and precision. This verification will be performed using state-of-the-art image classification benchmarks, such as ImageNet and also locally available classification test cases. As such, very good knowledge of neural network theory is required, along with substantial experience in deep learning tools such as Theano/Lasagne, Torch, or Tensorflow.

The characteristics of certain deep learning workloads (e.g. multi-layer perceptrons, convolutional neural networks, hierarchical temporal memories), are being gradually picked up by the hardware design community in an attempt to create efficient integrated solutions using non-volatile memory elements. This position will involve algorithm refinements on neural networks, so that these can be mapped optimally on non-volatile memory fabrics. This will involve verification of system-level choices related to neural network topology and precision. This verification will be performed using state-of-the-art image classification benchmarks, such as ImageNet and also locally available classification test cases. As such, very good knowledge of neural network theory is required, along with substantial experience in deep learning tools such as Theano/Lasagne, Torch, or Tensorflow.

Requirements:

You have a PhD degree in Electrical Engineering, Computer Architectures or similar. The ideal candidate should be knowledgeable in basic computer architecture concepts, including but not limited to Field Programmable Gate Arrays (FPGAs), in-/out-of-order execution, performance analysis, cache protocols, and memory hierarchies. As a result, the candidate is expected to have successfully completed graduate studies in the fields of Deep Learning and/or Computer Architecture. Exhibiting a coding repository on Deep Leaning tools or proven related design experience will be considered a substantial asset for prospective candidates. Finally, the candidate is expected to perform efficiently in a heavily inter-disciplinary field, exercise excellent oral and written communication skills, and report concisely on technical activities.

This postdoctoral position is funded by imec through KU Leuven. Because of the specific financing statute which targets international mobility for postdocs, only candidates who did not stay or work/study in Belgium for more than 24 months in the past 3 years can be considered for the position (short stays such as holiday, participation in conferences, etc. are not taken into account).